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检索条件"主题词=convolutional neural nets"

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Text detection method of signage image based on attention mechanism and SPP

Text detection method of signage image based on attention me...

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9th International Symposium on Test Automation & Instrumentation (ISTAI 2022)

作者： Q. Sun J. Zhang Z. Liu S. Li X. Tao T. Fan School of Electronic Engineering Jiangsu Ocean University Lianyungang People's Republic of China

Signage contains rich text information, which plays an important role in people's sightseeing. Aiming at the characteristics of signage images, a text detection method of signage images is proposed based on attention mechanism and space pyramid pooling (SPP). The proposed method improves the detection accuracy of the model by introducing the convolutional block attention module(CBAM) and adding parallel SPP paths in the improved feature pyramid networks (FPN) to enhance the textual information contained in the fused features. The experimental results show that the accuracy, recall rate and F value of the proposed method are 97.2%, 92.2% and 94.6% respectively, which are 0.8%, 1.8% and 1.3% higher than those of the original method DBNet, which proves the effectiveness of the improvement. On the public datasets ICDAR 2015 and MSRA-T500, the proposed method is also superior to other advanced methods.

关键词： convolutional neural nets image classification object detection text detection

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Identifying Interesting Moments in Controllers Work Video via Dimensionality Reduction

Identifying Interesting Moments in Controllers Work Video vi...

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International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT)

作者： Kristofer Krus Tatiana Polishchuk Valentin Polishchuk Communications and Transport Systems ITN Linkoping University Sweden

ISBN: (数字)9781728153803

ISBN: (纸本)9781728153810

We explore use of machine learning in automating the discovery of meaningful time intervals in video data. We combine convolutional neural Networks and Principal Component Analysis in order to zoom-in on interesting moments in hours-long videos of air traffic controllers work. Experimental results for air traffic control tower at Stockholm Bromma airport confirm feasibility of our approach. The method may be consequently used to single out workload-influencing factors, incident investigation and other post-operational analysis of controllers performance.

关键词： air traffic control airports convolutional neural nets learning (artificial intelligence) principal component analysis traffic engineering computing video signal processing Air Traffic Control principal components analysis Airports Video signal processing Controllers dimensionality reduction Videotapes engineering computing Air traffic controllers Moment of force component analysis video data

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Diagnosis of Induction Motor Faults Using Frequency Occurrence Image Plots—A Deep Learning Approach

Diagnosis of Induction Motor Faults Using Frequency Occurren...

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IEEE UP Section Conference on Electrical Computer and Electronics (UPCON)

作者： Arnab Nandi Souvik Biswas Kaniska Samanta Sayanjit Singha Roy Soumya Chatterjee St. Thomas' College of Engineering and Technology Kolkata India Techno India University Kolkata India

ISBN: (纸本)9781728134567

Accurate diagnosis of induction motor faults is important for reliable and safe operation of industrial processes. Majority of the faults which occur in induction motors are mainly diagnosed using motor current signature analysis. However, the accuracy of fault detection depends on selection of suitable features from motor current, the failure of which may result in incorrect interpretation. Considering the aforesaid fact, this paper presents an image processing aided deep learning framework for reliable diagnosis of induction motor faults, which eliminates the need of separate feature extraction stage. To this end, the motor current signals under different types of fault conditions were procured and were subsequently processed into frequency occurrence plots. The frequency occurrence image plots for different fault scenarios were finally used as inputs to a deep convolution neural network for the purpose of classification. Transfer learning technique was adopted to reduce the computation time of Convolution neural Network and classification of motor faults was done at five different loading conditions. Four types of classification tasks have been addressed here and comprehensive analysis was done using a variety of CNN architectures. It has been observed that the proposed method returns a highest mean classification accuracy of 96.67% in segregating different types of faults which can be implemented in real-life for condition monitoring of induction motors.

关键词： condition monitoring convolutional neural nets fault diagnosis feature extraction image classification induction motors learning (artificial intelligence) power engineering computing power system faults induction motors Power system faults Power engineering computing Condition monitoring Feature extraction Image classification motor fault faults motor current Fault diagnosis motor current signature analysis Defect detection fault condition

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Deep Learning for Inexpensive Image Classification of Wildlife on the Raspberry Pi

Deep Learning for Inexpensive Image Classification of Wildli...

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Brian H. Curtin Suzanne J. Matthews U.S. Military Academy West Point NY USA

ISBN: (纸本)9781728138862

Animal conservationists need unobtrusive methods of observing and studying wildlife in remote areas. Many commercial options for wildlife observation are expensive, obtrusive, or sub-optimal in remote environments. In this paper, we explore the viability of a Raspberry Pi-based camera system augmented with a deep learning image recognition model for detecting wildlife of interest. Unlike traditional sensor nodes that would have to transmit every captured image, localized image recognition enables only pictures of desired animals to be transferred to the user. For the purposes of this study, we use TensorFlow and Keras to create a convolutional neural network that runs on a Raspberry Pi 3B+. We trained the model on nearly 3,600 images gathered from publicly available image databases that are split into three classes. Our experiments suggest that our system can detect snow leopards with between 74 percent and 97 percent accuracy. We believe that our results show the viability of employing deep learning image recognition models on the Raspberry Pi to create an inexpensive system to observe wildlife.

关键词： convolutional neural nets feature extraction image classification learning (artificial intelligence) visual databases wireless sensor networks Image classification visual databases wildlife Image recognition Feature extraction Wireless sensor networks Raspberries Percent Proportional integral Snow Leopards Viability

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Camera Module Defect Detection Using Gabor Filter and convolutional neural Network

Camera Module Defect Detection Using Gabor Filter and Convol...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Sungrok Kim Taein Jeon Jeonghyun Yeo Yunhee Lee CammSys Corporation Incheon Republic of Korea

ISBN: (数字)9781728162898

ISBN: (纸本)9781728162904

Defect detection in camera module is closely related to the yield of camera module production line. As a result, many researches are being done regarding defect detection in camera modules. Conventional methods for detecting defects use template matching or edge filter with lens shading algorithm applied as preprocessing. Template matching method requires a non-defective image to use as a template which is not easy to acquire. Also, due to some defects occurring identical to noise most of the methods were only able to detect prominent defects. To overcome this problem, we propose a defect detecting method which can detect weak defects using a single image. The proposed method detects PP (Pixel Particle) defects by absolute differencing the original image with the blurred image of itself. Stain and crack defects are detected by dividing the image into subimages then comparing each subimage with the nearby subimages. Detected defect areas are fed to convolutional neural network for classification and verification. Experimental results show that the proposed method was able to detect even weak defects while correctly classifying the detected defects.

关键词： cameras convolutional neural nets crack detection Gabor filters image matching image texture inspection production engineering computing Gabor filters Defect detection Cameras production engineering computing image texture image matching crack detection subimage TEMPLATE MATCHING edge filter image blur Inspection

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Region-level arabic dialect identification using deep learning models

Region-level arabic dialect identification using deep learni...

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The 2nd International Conference on Distributed Sensing and Intelligent Systems (ICDSIS 2021)

作者： A. A. Sayadi A. Abulohoom S. A. Uddin M. Lataifeh A. Elnagar Dept. of Computer Science University of Sharjah Sharjah UAE

The automatic classification of the Arabic language dialects is the preliminary step towards establishing many dialectal-sensitive Arabic natural language processing tasks. Arabic dialect identification entails predicting the dialects associated with specific textual inputs and classifying them under their respected labels. In this paper, we propose a novel approach by merging several distinct datasets to obtain a large, diverse, and bias-free dialectal corpus. Our dataset is a collection of parallel sentences translated into multiple dialects (MADAR), as well as tweets gathered from Twitter users (NADI-2020, NADI-2021, QADI, & ARAP-Tweet 2.0). The collected dataset is classified into seven labels: Gulf, Levant, Iraq, Maghreb, Nile Basin, Yemen, and Modern Standard Arabic. The merged dataset was cleansed to produce Arabic sentences free of punctuation, non-Arabic characters, numbers, repeating characters, empty lines, and elongation characters. We obtained a high dialectal classification accuracy using a new Word2vec embedding model on the merged dialectal dataset. Seven deep learning systems were trained on a balanced subset of the dataset. These models are convolutional neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional-LSTM (BILSTM), Bidirectional-GRU (BIGRU), convolutional-LSTM (CLSTM), and convolutional-GRU (CGRU). Single-label classification tests ran on these trained models acquired a minimum accuracy of 77.90% (CNN) and a maximum accuracy of 81.52% (BILSTM). We further evaluated the accuracy of tests ran for short and long sentences with 87.52% attained accuracy for classifying short sentences with CGRU and 94.06% for long sentences using BIGRU, both of which are indications of proposed approach efficacy to move forward.

关键词： convolutional neural nets deep learning (artificial intelligence) natural language processing pattern classification recurrent neural nets social networking (online) text analysis

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Deep convolutional neural Networks for Breast Cancer Detection

Deep Convolutional Neural Networks for Breast Cancer Detecti...

引用

IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Ankit Roy FCPS Westfield High School Chantilly USA

ISBN: (纸本)9781728138862

Breast cancer is one of the main causes of cancer death worldwide. In the midst of the treatment of different disorders and diseases, one critical aspect in saving a patient is early detection. Correct detection and assessment of mammograms is hindered by human-error and inter-observer variations between pathologists. Existing convolutional neural network structures have shown promise in detection, but are hindered in their requirements for very large datasets to train on. The purpose of this paper is to explore a streamlined method classification of hematoxylin and eosin (H&E) stained tissue cancer mammograms into non-carcinomas and carcinomas using a small training set. This is done by the creation of more sample sets through changing elements of the data such as shear ratio and rotation. We assumed a 4-layer DCNN (deep convolutional neural network). We first train the DCNN with our augmented dataset, increasing dataset size by x200. We implement a highly accurate and reduced chance of overfitting gradient boosting algorithm. The overall classification accuracy of benign versus malignant was 88%.

关键词： biological organs cancer convolutional neural nets image classification learning (artificial intelligence) mammography medical image processing tumours biological organs Digital Mammography Medical image processing Image classification Tumours Interobserver Variation neural network cancer test Breast Cancer Dataset

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Target recognition of radar HRRP based on AUMAP segmentation and improved convolutional neural network

Target recognition of radar HRRP based on AUMAP segmentation...

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IET International Radar Conference (IET IRC 2020)

作者： M. Yin J. Zhang C. Zhang College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Sheng Tai West Road Jiang Ning District Nanjing 211106 China

In the high-resolution range profile (HRRP) recognition, segmentation can obtain the target attitude angle of HRRP data, and the methods of segmenting the HRRP data have a significant impact on improving the performance of recognition. Besides, the way of the feature extraction can enhance the separability among the HRRP data and improve the generalization of the classifier. To manage these practical problems, this paper puts forward a new recognition framework containing three steps. Firstly, HRRP data is segmented by Adaptive Uniform Manifold Approximation and Projection (AUMAP). Secondly, Key regions of the bispectra are reshaped through bispectra analysis to obtain additional data as input to the convolutional neural network (CNN). Thirdly, the loss function of CNN is modified to solve the imbalance of HRRP samples and improve the stability of the algorithm. Experimental results verify the effectiveness of the framework under the support of HRRP measurement data.

关键词： feature extraction radar target recognition radar resolution radar computing convolutional neural nets signal classification

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PLANT LEAF SEGMENTATION USING A REFINED FULLY convolutional NETWORK

PLANT LEAF SEGMENTATION USING A REFINED FULLY CONVOLUTIONAL ...

引用

Jiangsu Annual Conference on Automation (JACA 2020)

作者： L. Wang J. Sun W. Tan X. Sun X. Wu J. Shen Y. Hu Jiangsu University Zhenjiang China Ecovacs Suzhou China

Traditional segmentation method has the problem that background noise disturbs to the accuracy of segmentation. To improve the segmentation accuracy, an improved fully convolutional network (FCN) for plant leaf segmentation is proposed in this paper. In order to realize the rapid and accurate segmentation of plant leaves, firstly, the original skip connection structure was improved by reducing the number of feature maps in convolution layer C6. Secondly, features of the shallower layers were further integrated. Thirdly, the direct-connected structure (Direct-FCNs) was adopted by removing some layers. Moreover, the feature maps reduced by the VGG16 model were up-sampled to the original image size by de-convolution. Therefore, several improved models were obtained. Finally, much images were divided into training set and test set. The proposed method is compared with other traditional methods. It can be observed from all the experimental results that the refined model has higher segmentation accuracy, less parameters and stronger robustness, which is able to be used for the automatic segmentation of plant leaves.

关键词： image segmentation learning (artificial intelligence) convolutional neural nets botany biology computing deconvolution image sampling

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An Object Detection Network for Embedded System

An Object Detection Network for Embedded System

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Yanpeng Sun Chenlu Wang Lele Qu College of Electronic Information Shenyang Aerospace University Shenyang China

ISBN: (纸本)9781728152103

Object detection in images has a wide range of applications in various fields. However, many of the convolutional neural networks recently proposed have higher requirements on computing resources while achieving higher precision, which cannot guarantee good real-time performance on embedded platforms with limited resources. This paper proposed an object detection network suitable for embedded systems. The M-YOLO (Mobile-YOLO) model proposed in this paper combines depthwise separable convolution and residual blocks in feature extraction layers, which helps to reduce the amount of computation of the network. Multi-scale feature fusion is applied to the output layers to improve the accuracy. Experiments show that the M-YOLO model has 9.68M FLOPs (Floating Point Operations), which is about 22% of Tiny-YOLO model. The accuracy of the network reaches 56.61% on the PASCAL VOC dataset, and the speed in ARM is over 3 times faster than Tiny-YOLO model. The network is more suitable for embedded systems.

关键词： convolutional neural nets embedded systems feature extraction image fusion object detection Embedded systems Object detection image fusion Feature extraction

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